The guide offers an overview of the concept or use case, an explanation of a traditional solution to achieve it, key open-source projects, and a highlight of how major cloud providers are using open source to address the use case. Todd Moore, VP of open technology at IBM, and Christopher Ferris, IBM Fellow and IBM’s CTO of open technology wrote in a blog post:

“Because every major cloud platform uses open-source software in their infrastructure, developing skills related to open technology makes developers more desirable to potential employers and helps developers compete in hybrid environments; that is, those that provide the ability and flexibility of running parts or all of your cloud solution and services on-premises and/or on a public cloud, and/or in multiple clouds.”

A recent O’Reilly survey that IBM commissioned in late 2020 found that the most desired of the open-source skills are around Linux, artificial intelligence and machine learning, and data storage, each of which IBM now has guides for.

The guide aims to answer questions on how these skills translate to developing hybrid cloud environments inclusive of the major cloud providers.

According to the company, to close the current global skills gap, education and training systems need to evolve along with market demands.

As part of its new collaboration, global organizations will leverage IBM SkillsBuild, an online learning program designed to get job seekers ready for the professional workforce within three to six months. It will offer technical skills, badges, and credentials recognized by the market. It will focus on underserved populations such as veterans, women, minorities, refugees, and unemployed young adults. Arvind Krishna, chairman and CEO of IBM said:

“Closing the global skills gap is one of the most pressing issues of our time. That is why I’m proud of these collaborations, which will help people of all backgrounds acquire the skills they need to thrive in a fast-changing global economy.”

The company also announced that the workforce solutions company ManpowerGroup will work to connect with these job seekers to offer real career opportunities in cloud and infrastructure, cybersecurity, data analysis, digital workspace and enterprise application positions. In addition, ManpowerGroup will provide assessment, coaching and personalized support through its talent agents. Curtis Price, vice president of Social, Environmental Responsibility and Ethics at IDC also commented:

“We are seeing an increased demand for new skills needed in future tech jobs like artificial intelligence, cybersecurity, data analytics, and cloud computing. IDC believes that the partnerships IBM has announced are essential to help meet the future demands for tech jobs. However, the partnership with ManpowerGroup is a perfect complement to IBM SkillsBuild’s mission of providing training for underrepresented groups as it provides meaningful career opportunities to badged IBM SkillsBuild participants.”

Through these efforts, IBM hopes to re-skill 500,000 people by the end of the year; provide 15,00 people with special program-based learning experiences; and secure 7,000 jobs across different industries.

IBM has created an open-source Python library, called Uncertainty Quantification 360 or UQ360, that provides developers and data scientists with algorithms to quantify the uncertainty of machine learning predictions, with the goal of improving the transparency of machine learning models and trust in AI.

Available from IBM Research, UQ360 aims to address problems that result when AI systems based on deep learning make overconfident predictions. With the Python toolkit, users are provided algorithms to streamline the process of quantifying, evaluating, improving, and communicating the uncertainty of predictive models. Currently, the UQ360 toolkit provides 11 algorithms to estimate different types of uncertainties, collected behind a common interface. IBM also provides guidance on choosing UQ algorithms and metrics.

IBM stressed that overconfident predictions of AI systems can have serious consequences. Examples cited included a chatbot being unsure of when a pharmacy closes, resulting in a patient not getting needed medication, and the life-or-death importance of reliable uncertainty estimates in the detection of sepsis. UQ exposes the limits and potential failure points of predictive models, enabling AI to express that it is unsure and increasing the safety of deployment.

Previous IBM efforts to advance trust in AI have included the AI Fairness 360 toolkit, which mitigates bias in machine learning models; the Adversarial Robustness Toolbox, which is a Python library for machine learning security; and the AI Explainability 360 toolkit, that helps users comprehend how machine learning models predict labels.

Fortran recently popped up again in a ranking of the most popular programming languages, albeit in 20th place. This resurgence has been explained by the huge need for scientific number crunching; something that Fortran is very good at.

But few would argue that Fortran has an especially rosy future ahead right now, as scientists are flocking to newer languages such as Python or Julia.

Fortran itself is still actively developed through two main bodies: the US Fortran standards committee, J3, which is the primary influencer of standards set by the worldwide Fortran body, WG5. Intel, Nvidia, Arm, IBM, AMD, the DoE, NASA and others are represented on J3. Tom Clune, lead for the software integration team and the Global Modeling and Assimilation Office at NASA’s Goddard Space Flight Center commented:

“I think Fortran today is in a very difficult position. On the one hand, there is any number of newer languages that have features that appear to be desirable for problems that were traditionally the primary domain of Fortran. The current market has resulted in a shrinking number of modern Fortran compilers – a trend that is arguably now reversing – and decreasing budgets for Fortran compiler developers.”

Fortran has undergone several major updates since 2000, including the F2003 and F2008 releases, and another in 2018. The next two updates are referred to as F202X and F202Y, but they’re years away.

Clune argues Fortran’s survival is a benefit to the science community. The core parts of Fortran can generally be learned on the job and require a bit less sophistication than languages like C++, he notes. And for scientists, switching languages introduces a huge risk of introducing subtle changes to the analysis they are doing.

“There are certainly new projects being written in Fortran. Someone will add new features and before you know it there will be another big Fortran application out there.”

However, Clune concedes it’s becoming rarer. But Fortran still has benefits over new languages, he argues. According to him, Fortran has exceptionally good built-in support for numerical calculations and array manipulation, which is particularly important for scientists and engineers and Python and Java are generally perceived as being slower.

Anaconda is the world’s most popular Python distribution platform and boasts over 25 million users worldwide. This announcement is the latest part of IBM’s effort to bring popular data science frameworks and libraries to its enterprise platforms.  Barry Baker, VP of Product Management for IBM Z & LinuxONE, commented:

“Data scientists who already know and love Anaconda can now expand their open-source data science experience to include IBM Z & LinuxONE while continuing to work with their favourite tools and frameworks like conda, XGBoost and SciKit-Learn. This expands and enables choice in AI frameworks and tooling for end-to-end data science directly on the platform, including development, training, testing and production. Data scientists can benefit from the security capabilities, high availability and scalability of the IBM Z & LinuxONE platforms when implementing AI deployments targeting time-sensitive workloads or transactions when they are taking place.”

According to new research commissioned by IBM in partnership with Morning Consult, 90% of respondents said that being able to build and run AI projects wherever their data resides is important.  Workloads running on IBM Z & LinuxONE often need to adhere to strict latency and SLA requirements to support transactions that are key to our modern life such as online purchases. With Anaconda for Linux on Z & LinuxONE, organizations can perform AI analysis in close proximity to their data, addressing latency to deliver insights where and when they are needed.

Customers can start using Anaconda Individual Edition and Anaconda Commercial Edition by downloading the Individual Edition or Miniconda installer, and following the associated installation documentation:

• Individual Edition
• Miniconda

For more information on using Anaconda Individual or Commercial Edition, you can visit docs.anaconda.com.

This year’s competition invites the world’s software developers and innovators to combat climate change with open source-powered technology. 

Now in its fourth year, the Initiative has grown to more than 400,000 developers and problem solvers across 179 nations and has generated more than fifteen thousand applications.

Call for Code aims to drive immediate and lasting humanitarian progress around the world through the creation of practical applications built on open source-powered software, including Red Hat OpenShift, IBM Cloud, IBM Watson, IBM Blockchain, atmospheric data from IBM’s Weather Company, developer resources and APIs from partners like Intuit and New Relic.

The diverse and like-minded global ecosystem supports Call for Code to continue expanding. It includes UN World Food Programme Innovation Accelerator experts, Arrow Electronics, Black Girls Code, Caribbean Girls Hack, Clinton Foundation, Clinton Global Initiative University, Ingram Micro, Intuit, Kode With Klossy, NearForm, United Nations Office for Disaster Risk Reduction, United Way, and World Institute on Disability.

Access to clean water is one focus of this year’s Call for Code competition, a global initiative to combat climate change. Photo Credits: The Weather Channel

To help take on climate change, IBM is announcing new partnerships this year with Heifer International and charity: water. Heifer International joins the Call for Code movement, contributing their world-renowned expertise in mitigating hunger and poverty by investing in local farmers and their communities.  David Gill, Senior Director of Technology Innovation for Heifer International said:

“Smallholder farmers produce the majority of the world’s food and are on the frontlines of the climate crisis. With access to information and technology, they can make informed decisions on what to grow and when, enabling them to increase their incomes, while feeding the world. We’re excited to join IBM and the developer and open source communities in supporting these solutions that have the potential to increase access to water, food, and markets for millions of people around the world.”

IBM’s multi-pronged advocacy for environmental sustainability dates back decades. Earlier this year, IBM announced it will achieve net-zero greenhouse gas emissions by 2030 by prioritizing reductions in its emissions, energy efficiency efforts, and increased clean energy use across the more than 175 countries where it operates. Ruth Davis, director of Call for Code, IBM noted:

“Climate change is one of the most pressing issues of our time, and we must apply our collective ingenuity and cutting-edge technologies to make a lasting difference. Together with our ecosystem of partners, IBM will work with the winning team to incubate and deploy their solution in communities where it’s most needed, just as we’ve done with past winners. I encourage every developer and innovator around the world to seize this opportunity through Call for Code to change our climate trajectory.”

David Clark, Photo Credits: IBM Newsroom

The winning solution from last year, Agrolly, is an app designed to support small farmers by providing climate and crop predictions and recommendations. Since October, its team has expanded their solution to new markets and provided hands-on training to more than 500 rural farmers who are testing and using the app to fight the effects of climate change. Agrolly is also working with the IBM Service Corps on a deployment plan to improve and test their technology in the coming months.

David Clark, CEO of David Clark Cause, and Creator of Call for Code said:

“The winning team from each Call for Code Global Challenge receives $200,000, and support from the IBM Service Corps, technical experts, and ecosystem partners to incubate their technology, open source their code to make it available for anyone to use, and deploy their solution on the ground in communities around the world. This year the competition focuses on three sub-themes that are key to combating climate change: clean water and sanitation; zero hunger; and responsible production and green consumption.”

There are now more than 30 generating solutions that are being incubated and field-tested in a series of deployments. Because of the UN World Water Day, the Linux Foundation announced that another Call for Code solution, Liquid Prep, will be hosted at the Foundation so developers worldwide can contribute their mobile and weather skills to help farmers optimize water usage during droughts.

“QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer” 

This paper presents the first “medium-scale” implementation of common NLP tasks. Completed on an IBM quantum computer, the experiment, which instantiated sentences as parameterised quantum circuits, embeds word meanings as quantum states which are “entangled” according to the grammatical structure of the sentence.

Bob Coecke, CQC’s Chief Scientist and also the Head of CQC’s QNLP project, noted:

“We are working on an immensely ambitious project at CQC that is aimed at utilising quantum computers, as they scale, to move beyond expensive black-box mechanisms for NLP to a paradigm where we become more effective, more accurate and more scalable in an area of computer science that epitomises artificial intelligence. Having made considerable progress already on our ‘quantum-native’ brand of compositional NLP, we are now moving beyond our initial research and working on applications that can be developed in sync with timelines provided by quantum computing hardware companies such as IBM, Honeywell, Google and others. Equally, at a time when quantum computing is becoming a topic of general interest, those of us who are working within this sector must provide verifiable results. Our record of publication at CQC strives at all times to meet these exacting standards – we are science-led and enterprise-driven.”

The paper builds on prior proof-of-concept work and, significantly, achieves convergence for the far larger datasets that are employed here. One of the objectives of the CQC team is to describe Quantum Natural Language Processing (QNLP) and its results in a way that is accessible to NLP researchers and practitioners thus paving the way for the NLP community to engage with a quantum encoding of language processing.

First, you need to understand the personality of your community because as we all know, each community has its own way of working. Read and respect the community’s Code of Conduct, and if they don’t have one… well, that might be a red flag.

Another very important rule is to behave in a respectful and professional manner and treat others the way you want to be treated. Start with small steps, so that you can build up trust in your community. In the beginning, you have to contribute with small changes before you will be trusted to make larger ones.

Make sure that your codes are always clean, manageable and tested because we are all earning respect and eminence based on the content that we provide. Including tests with your code is always helpful so the reviewer can validate that the code is clean. You also need to have a maintenance plan. As soon as your code is committed, you need to be responsible for it or hand it off in a responsible way.

You need to be looking to other people for advice and offer your help in return. The experienced contributor is usually more willing to answer your questions and do code reviews of your work if you help them out.

Last but definitely not least, know your licence and stick to the rules. For instance, IBM prefers to contribute under permissive licenses such as the Apache v2, MIT, EPL, and BSD licenses. So, before you contribute to open source, make sure you understand the licenses and also the rules of your company. Talk less, do more, and prove your worth to the community and after all, remember that you are your reputation.

 The combination of the way you treat other people in an open-source community, your general attitude, and the code you put out point to who you are and whether you’re going to be a good community member or not.